We present an Integer Linear Program for exact inference under a maximum coverage model for automatic summarization. We compare our model, which operates at the subsentence or "concept"-level, to a sentencelevel model, previously solved with an ILP. Our model scales more efficiently to larger problems because it does not require a quadratic number of variables to address redundancy in pairs of selected sentences. We also show how to include sentence compression in the ILP formulation, which has the desirable property of performing compression and sentence selection simultaneously. The resulting system performs at least as well as the best systems participating in the recent Text Analysis Conference, as judged by a variety of automatic and manual content-based metrics.

11We analyze and compare two different methods for unsupervised extractive spontaneous speech summarization in the meeting 12 domain. Based on utterance comparison, we introduce an optimal formulation for the widely used greedy maximum marginal relevance 13 (MMR) algorithm. Following the idea that information is spread over the utterances in form of concepts, we describe a system which 14 finds an optimal selection of utterances covering as many unique important concepts as possible. Both optimization problems are for-15 mulated as an integer linear program (ILP) and solved using public domain software. We analyze and discuss the performance of both 16 approaches using various evaluation setups on two well studied meeting corpora. We conclude on the benefits and drawbacks of the 17 presented models and give an outlook on future aspects to improve extractive meeting summarization.

We introduce a model for extractive meeting summarization based on the hypothesis that utterances convey bits of information, or concepts. Using keyphrases as concepts weighted by frequency, and an integer linear program to determine the best set of utterances, that is, covering as many concepts as possible while satisfying a length constraint, we achieve ROUGE scores at least as good as a ROUGEbased oracle derived from human summaries. This brings us to a critical discussion of ROUGE and the future of extractive meeting summarization.

In this paper we present an overview of MultiLing 2015, a special session at SIGdial 2015. MultiLing is a communitydriven initiative that pushes the state-ofthe-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. There were in total 23 participants this year submitting their system outputs to one or more of the four tasks of MultiLing: MSS, MMS, OnForumS and CCCS. We provide a brief overview of each task and its participation and evaluation.